Why federated learning will do little to overcome the deeply embedded biases in clinical medicine-Reference-Cited by-同舟云学术

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Why federated learning will do little to overcome the deeply embedded biases in clinical medicine

Published:2024-06-03 Issue:8 Volume:50 Page:1390-1392
ISSN:0342-4642
Container-title:Intensive Care Medicine
language:en
Short-container-title:Intensive Care Med

Author:

Sauer Christopher Martin^ORCID,Pucher Gernot^ORCID,Celi Leo Anthony^ORCID

Funder

UMEA Clinician Scientist/ DFG

Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen

National Institute of Health

National Science Foundation

Universitätsklinikum Essen

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00134-024-07491-8.pdf

Reference4 articles.

1. van Genderen ME, Cecconi M, Jung C (2024) Federated data access and federated learning: improved data sharing, AI model development, and learning in intensive care. Intensive Care Med. https://doi.org/10.1007/s00134-024-07408-5

2. Wong A-KI, Charpignon M, Kim H et al (2021) Analysis of discrepancies between pulse oximetry and arterial oxygen saturation measurements by race and ethnicity and association with organ dysfunction and mortality. JAMA Netw Open 4:e2131674. https://doi.org/10.1001/jamanetworkopen.2021.31674

3. Teotia K, Jia Y, Link Woite N et al (2024) Variation in monitoring: glucose measurement in the ICU as a case study to preempt spurious correlations. J Biomed Inform 153:104643. https://doi.org/10.1016/j.jbi.2024.104643

4. Abdelmalek FM, Angriman F, Moore J et al (2024) Association between patient race and ethnicity and use of invasive ventilation in the United States. Annals ATS 21:287–295. https://doi.org/10.1513/AnnalsATS.202305-485OC

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1. Federated learning: a step in the right direction to improve data equity;Intensive Care Medicine;2024-07-02

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